Process variability aware adaptive inspection and metrology

ABSTRACT

A defect prediction method for a device manufacturing process involving processing one or more patterns onto a substrate, the method including: determining values of one or more processing parameters under which the one or more patterns are processed; and determining or predicting, using the values of the one or more processing parameters, an existence, a probability of existence, a characteristic, and/or a combination selected from the foregoing, of a defect resulting from production of the one or more patterns with the device manufacturing process.

This application is the U.S. national phase entry of PCT patentapplication no. PCT/EP2016/051073, filed Jan. 20, 2016, which claims thebenefit of priority of U.S. Provisional Patent Application No.62/116,256, filed Feb. 13, 2015 and U.S. Provisional Application No.62/117,261, filed Feb. 17, 2015 , which are incorporated by referenceherein in their entireties.

TECHNICAL FIELD

The present description relates to a method of adjusting the performanceof a semiconductor manufacturing process. The method may be used inconnection with a lithographic apparatus.

BACKGROUND

A lithographic apparatus can be used, for example, in the manufacture ofintegrated circuits (ICs). In such a case, a patterning device (e.g., amask) may contain or provide a circuit pattern corresponding to anindividual layer of the IC (“design layout”), and this circuit patterncan be transferred onto a target portion (e.g. comprising one or moredies) on a substrate (e.g., silicon wafer) that has been coated with alayer of radiation-sensitive material (“resist”), by methods such asirradiating the target portion through the circuit pattern on thepatterning device. In general, a single substrate contains a pluralityof adjacent target portions to which the circuit pattern is transferredsuccessively by the lithographic apparatus, one target portion at atime. In one type of lithographic apparatuses, the circuit pattern onthe entire patterning device is transferred onto one target portion inone go; such an apparatus is commonly referred to as a stepper. In analternative apparatus, commonly referred to as a step-and-scanapparatus, a projection beam scans over the patterning device in a givenreference direction (the “scanning” direction) while synchronouslymoving the substrate parallel or anti-parallel to this referencedirection. Different portions of the circuit pattern on the patterningdevice are transferred to one target portion progressively. Since, ingeneral, the lithographic apparatus will have a magnification factor M(generally <1), the speed F at which the substrate is moved will be afactor M times that at which the projection beam scans the patterningdevice. More information with regard to lithographic devices asdescribed herein can be gleaned, for example, from U.S. Pat. No.6,046,792, incorporated herein by reference.

The lithographic apparatus may be of a type having two or more tables(e.g., two or more substrate table, a substrate table and a measurementtable, two or more patterning device tables, etc.). In such “multiplestage” devices the additional tables may be used in parallel, orpreparatory steps may be carried out on one or more tables while one ormore other tables are being used for exposures. Twin stage lithographicapparatuses are described, for example, in U.S. Pat. No. 5,969,441,incorporated herein by reference.

Prior to transferring the circuit pattern from the patterning device tothe substrate, the substrate may undergo various procedures, such aspriming, resist coating and a soft bake. After exposure, the substratemay be subjected to other procedures, such as a post-exposure bake(PEB), development, a hard bake and measurement/inspection of thetransferred circuit pattern. This array of procedures is used as a basisto make an individual layer of a device, e.g., an IC. The substrate maythen undergo various processes such as etching, ion-implantation(doping), metallization, oxidation, chemo-mechanical polishing, etc.,all intended to finish off the individual layer of the device. Ifseveral layers are required in the device, then the whole procedure, ora variant thereof, is repeated for each layer. Eventually, a device willbe present in each target portion on the substrate. These devices arethen separated from one another by a technique such as dicing or sawing,whence the individual devices can be mounted on a carrier, connected topins, etc.

As noted, lithography is a central step in the manufacturing of ICs,where patterns formed on substrates define functional elements of theICs, such as microprocessors, memory chips etc. Similar lithographictechniques are also used in the formation of flat panel displays,micro-electro mechanical systems (MEMS) and other devices.

SUMMARY

Disclosed herein is a computer-implemented defect prediction method fora device manufacturing process involving processing one or more patternsonto a substrate, the method comprising: determining values of one ormore processing parameters under which the one or more patterns areprocessed; and determining or predicting, using the values of the one ormore processing parameters, existence, probability of existence, acharacteristic, and/or a combination selected from the foregoing, of adefect produced from the one or more patterns with the devicemanufacturing process.

Disclosed herein is a computer-implemented defect prediction method fora device manufacturing process, the method comprising: determiningvalues of one or more processing parameters under which one or morepatterns are processed onto an area of a substrate; and determining orpredicting, using the values of the one or more processing parameters,existence, probability of existence, a characteristic, and/or acombination selected from the foregoing, of a defect, in the area,resulting from production of the one or more patterns with the devicemanufacturing process.

Disclosed herein is a computer-implemented defect prediction method fora device manufacturing process involving processing one or more patternsonto a substrate, the method comprising: determining values across thesubstrate of one or more processing parameters of the devicemanufacturing process under which one or more patterns are processedonto the substrate, and identifying one or more areas, utilizing thevalues of the one or more processing parameters, as having, or havingincreased probability of existence of, a defect resulting fromproduction with the device manufacturing process.

Disclosed herein is a computer-implemented defect prediction method fora device manufacturing process, the method comprising: determiningvalues of one or more processing parameters under which one or morepatterns are processed onto an area of a substrate; and determining orpredicting, using the values of the one or more processing parameters, asub-area of the substrate at which a defect exists, or has an increasedprobability of existing, resulting from production of the one or morepatterns with the device manufacturing process.

Disclosed herein is a device for selecting areas to be inspected on asubstrate, the device configured to obtain values of one or moreprocessing parameters under which one or more patterns are processedonto an area of the substrate; and the device configured to select anarea for inspection, if existence, probability of existence, acharacteristic, and/or a combination selected from the foregoing, of adefect in the area resulting from production of the one or morepatterns, meets one or more criteria, wherein the existence, probabilityof existence, characteristic, and/or combination selected from theforegoing, is determined or predicted using the values of the one ormore processing parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

The above aspects and other aspects and features will become apparent tothose ordinarily skilled in the art upon review of the followingdescription of specific embodiments in conjunction with the accompanyingfigures, wherein:

FIG. 1 is a block diagram of various subsystems of a lithography systemaccording to an embodiment;

FIG. 2 shows a flow chart for a method that determines or predicts,using a condition (e.g., as manifested as values of one or moreprocessing parameters) under which a pattern is processed, theexistence, probability of existence, a characteristic, and/or acombination selected from the foregoing, of a defect produced from thepattern with the device manufacturing process;

FIG. 3 shows exemplary sources of the one or more processing parameters;

FIG. 4A shows an implementation of step 292 of FIG. 2;

FIG. 4B shows a further implementation of step 292 of FIG. 2;

FIG. 5 shows an exemplary flow that uses the method of FIG. 2;

FIG. 6 shows another exemplary flow that uses the method of FIG. 2;

FIG. 7 is a block diagram of an example computer system in whichembodiments can be implemented;

FIG. 8 is a schematic diagram of another lithographic apparatus;

FIG. 9 is a more detailed view of the apparatus in FIG. 8;

FIG. 10 is a more detailed view of the source collector module SO of theapparatus of FIG. 8 and FIG. 9.

DETAILED DESCRIPTION

Embodiments will now be described in detail with reference to thedrawings, which are provided as illustrative examples so as to enablethose skilled in the art to practice the embodiments. Notably, thefigures and examples below are not meant to limit the scope to a singleembodiment, but other embodiments are possible by way of interchange ofsome or all of the described or illustrated elements. Whereverconvenient, the same reference numbers will be used throughout thedrawings to refer to same or like parts. Where certain elements of theseembodiments can be partially or fully implemented using knowncomponents, only those portions of such known components that arenecessary for an understanding of the embodiments will be described, anddetailed descriptions of other portions of such known components will beomitted so as not to obscure the description of the embodiments. In thepresent specification, an embodiment showing a singular component shouldnot be considered limiting; rather, the scope is intended to encompassother embodiments including a plurality of the same component, andvice-versa, unless explicitly stated otherwise herein. Moreover,applicants do not intend for any term in the specification or claims tobe ascribed an uncommon or special meaning unless explicitly set forthas such. Further, the scope encompasses present and future knownequivalents to the components referred to herein by way of illustration.

As a brief introduction, FIG. 1 illustrates an exemplary lithographicapparatus 10. Major components are an illumination source 12, which maybe a deep-ultraviolet excimer laser source or other type of sourcesincluding extreme ultra violet (EUV) sources, illumination optics whichdefine the partial coherence (denoted as sigma) and which may includeoptics 14, 16 a and 16 b that shape radiation from the source 12; apatterning device (e.g., a mask or reticle) 18; and transmission optics16 c that project an image of the patterning device pattern onto asubstrate plane 29. An adjustable filter or aperture 20 at the pupilplane of the projection optics may restrict the range of beam anglesthat impinge on the substrate plane 29, where the largest possible angledefines the numerical aperture of the projection optics NA=sin(θ_(max)).

In a lithographic apparatus, a source provides illumination (i.e.radiation) to a patterning device; projection optics direct and shapesthe illumination, via the patterning device, onto a substrate. The term“projection optics” is broadly defined here to include any opticalcomponent that may alter the wavefront of the radiation beam. Forexample, projection optics may include at least some of the components14, 16 a, 16 b and 16 c. An aerial image (AI) is the radiation intensitydistribution on the substrate. A resist layer on the substrate isexposed and the aerial image is transferred to the resist layer as alatent “resist image” (RI) therein. The resist image (RI) can be definedas a spatial distribution of solubility of the resist in the resistlayer. A resist model can be used to calculate the resist image from theaerial image, an example of which can be found in commonly assigned U.S.Patent Application Publication No. US 2009-0157360,the disclosure ofwhich is hereby incorporated by reference in its entirety. The resistmodel is related only to properties of the resist layer (e.g., effectsof chemical processes which occur during exposure, PEB and development).Optical properties of the lithographic apparatus (e.g., properties ofthe illumination, the patterning device and the projection optics)dictate the aerial image. Since the patterning device used in thelithographic apparatus can be changed, it is desirable to separate theoptical properties of the patterning device from the optical propertiesof the rest of the lithographic apparatus including at least theillumination and the projection optics.

The term patterning device as employed in this text may be broadlyinterpreted as referring to generic patterning device that can be usedto endow an incoming radiation beam with a patterned cross-section,corresponding to a pattern that is to be created in a target portion ofthe substrate; the term “light valve” can also be used in this context.Besides the classic mask (transmissive or reflective; binary,phase-shifting, hybrid, etc.), examples of other such patterning devicesinclude:

-   -   a programmable mirror array. An example of such a device is a        matrix-addressable surface having a viscoelastic control layer        and a reflective surface. The basic principle behind such an        apparatus is that (for example) addressed areas of the        reflective surface reflect incident radiation as diffracted        radiation, whereas unaddressed areas reflect incident radiation        as undiffracted radiation. Using an appropriate filter, the said        undiffracted radiation can be filtered out of the reflected        beam, leaving only the diffracted radiation behind; in this        manner, the beam becomes patterned according to the addressing        pattern of the matrix-addressable surface. The matrix addressing        can be performed using suitable electronics. More information on        such mirror arrays can be gleaned, for example, from U.S. Pat.        Nos. 5,296,891 and 5,523,193, which are incorporated herein by        reference.    -   a programmable LCD array. An example of such a construction is        given in U.S. Pat. No. 5,229,872, which is incorporated herein        by reference.

Although specific reference may be made in this text to the use of theembodiments in the manufacture of ICs, it should be explicitlyunderstood that the embodiments has many other possible applications.For example, it may be employed in the manufacture of integrated opticalsystems, guidance and detection patterns for magnetic domain memories,liquid-crystal display panels, thin-film magnetic heads, etc. Theskilled artisan will appreciate that, in the context of such alternativeapplications, any use of the terms “reticle,” “wafer” or “die” in thistext should be considered as interchangeable with the more general terms“mask,” “substrate” and “target portion,” respectively.

In the present document, the terms “radiation” and “beam” are used toencompass all types of electromagnetic radiation, including ultravioletradiation (e.g. with a wavelength of 365, 248, 193, 157 or 126 nm) andEUV (extreme ultra-violet radiation, e.g. having a wavelength in therange 5-20 nm).

Various patterns of, or provided by, a patterning device may havedifferent process windows, i.e., a space of processing parameters underwhich a pattern will be produced within a specification. Examples ofpattern specifications (which relate to potential systematic defects)include one or more checks for necking, line pull back, line thinning,critical dimension, edge placement, overlapping, resist top loss, resistundercut and/or bridging. If a pattern is produced outside an applicablespecification, it is a defect. The process window of all or some(usually one or more patterns within a particular area) of the patternsof, or provided by, a patterning device may be obtained by merging(e.g., overlapping) the process windows of each of the individualpatterns. The process window of these patterns thus may be called anoverlapping process window (OPW). The boundary of the OPW containsboundaries of process windows of at least some of the individualpatterns. In other words, these individual patterns limit the OPW. Theseindividual patterns can be referred to as “process window limitingpatterns (PWLPs).” When controlling a lithography process, it ispossible and economical to focus on the PWLPs. When the PWLPs are notdefective, it is likely that none of the patterns is defective.

A pattern that is prone to producing a defect may be generally called apattern of interest (POI). A POI may include one or more PWLPs but isnot necessarily limited to one or more PWLPs. For example, a POI mayinclude one or more patterns that are empirically more likely to producea defect. Such a POI may be identified by subjecting patterns of thedesign layout to one or more empirical rules. A POI may include one ormore patterns that are not a PWLP but whose process window is close tothe boundary of the OPW. A POI may include one or more patterns thatproduced defects in the past (e.g., shown by actual inspection with ametrology tool). A POI may include one or more patterns that have a highor increased probability of producing a defect according to, forexample, a statistical model. High probability may include greater thanor equal to 10%, greater than or equal to 20%, greater than or equal to30%, greater than or equal to 40%, greater than or equal to 50%, greaterthan or equal to 60%, greater than or equal to 70%, greater than orequal to 80%, or greater than or equal to 90% of producing a defect. APOI may include one or more patterns that have certain geometricaltraits (e.g., size, shape, relative position to one or more otherpatterns, etc.).

Once a pattern is identified as a POI, it is desirable to inspect thatpattern using a suitable online or off-line metrology tool. However, ifthe number of POIs is too large, inspecting them will be very timeconsuming. Therefore, it is beneficial to have a method that can moreaccurately determine or predict whether a pattern imaged onto a givensubstrate under a given condition is a defect, thereby reducing thenumber of POIs to inspect and reducing the time inspection requires.Other than improving the method itself, considering more information mayhelp to improve the accuracy in the determination or prediction. Forexample, the method may consider the condition (e.g., as manifested asvalues of one or more processing parameters) under which a pattern is,or will be, processed. The condition may vary with position on asubstrate or with time (e.g., between substrates, between dies on asubstrate, etc.). Such variation may be caused by change in theenvironment such as temperature and/or humidity. Other causes of suchvariation may include drift in one or more components in the processingapparatus such as the illumination system, projection optics, substratetable, height variation of a substrate surface, etc. It would bebeneficial to be aware of such variation and its effects on whether aparticular pattern will produce a defect. For example, a pattern that isotherwise not likely to produce a defect may do so if it is imaged ontoan area of a substrate with abnormal topography (e.g., an elevated areaor a recessed area). Of course, a pattern that is very likely to producea defect might be “saved” if it is imaged onto such an area.

According to an embodiment, a method determines or predicts, using acondition (e.g., as manifested as values of one or more processingparameters) under which a pattern is processed, the existence,probability of existence, a characteristic, and/or a combinationselected from the foregoing, of a defect produced from the pattern withthe device manufacturing process.

FIG. 2 shows a flow chart of an embodiment of this method. In step 291,values of one or more processing parameters under which one or morepatterns are processed (e.g., imaged or etched onto a substrate) aredetermined. In an embodiment, the one or more patterns may be selectedexclusively among the PWLPs. In an embodiment, the one or more patternsmay be selected among all the patterns. The one or more processingparameters may be local—dependent on the location of the one or morepatterns, the die, or both. The one or more processing parameters may beglobal—independent of the location of the one or more patterns and thedies. One exemplary way to determine the one or more processingparameters is to determine the status of the lithographic apparatus. Forexample, one or more processing parameters such as laser bandwidth,focus, dose, an illumination shape parameter, a projection opticsparameter, etc., and/or spatial or temporal variation of one or morethese parameters, may be measured from the lithographic apparatus.Another exemplary way is to infer the one or more processing parametersfrom data obtained from metrology performed on the substrate, or from anoperator of the processing apparatus. For example, metrology may includeinspecting a substrate using a diffractive tool (e.g., ASML YieldStarmetrology tool), an electron microscope, or other suitablemeasurement/inspection tool. It is possible to obtain values of the oneor more processing parameters for any location on a processed substrate,including the identified POI. In an embodiment, the one or moreprocessing parameters include the depth of focus and/or the focus error(which is the difference between the actual focus to the best focus fora particular pattern). In an embodiment, the focus error may be obtainedfrom multiple sources, such as the lithographic apparatus, on-substratefocus metrology and/or topography measurement of the substrate. Focuserror may be attributed to topography of the substrate, drift of theprojection optics, drift of the illumination system (such as theradiation source), clamping of the substrate, a substrate-levelingsystem of the lithographic apparatus, and/or a combination selected fromthe foregoing. The values of the one or more processing parameters maybe compiled into a map—one or more lithographic parameters, or processconditions, as a function of location. Of course, values of one or moreother processing parameters may be represented as function of location,i.e., a map. In an embodiment, the values of the processing parametersmay be determined before, or desirably immediately before (e.g., noother patterns processed after determining the one or more processingparameters and before the one or more patterns are processed), orduring, processing the one or more patterns.

In step 292, existence, probability of existence, a characteristic,and/or a combination selected from the foregoing, of a defect the one ormore patterns produce is determined or predicted using the values of theone or more processing parameters under which the one or more patternsare processed. In an embodiment, one or more characteristics of the oneor more patterns can also be used in the determination or prediction. Inan embodiment, the determination or prediction is done without using anycharacteristic of the one or more patterns. This determination may besimply comparing the values of the one or more processing parameters andthe OPW of the one or more patterns—if the values fall within the OPW,no defect exists; if the values fall outside the OPW, at least onedefect is expected to exist. This determination may also be done using asuitable empirical model (including a statistical model). For example, aclassification model may be used to provide a probability of existenceof a defect. Another way to make this determination is to use acomputational model to simulate an image, or expected patterningcontours, that the one or more patterns produce under the values of theone or more processing parameters and evaluate (e.g., measure) one ormore image or contour parameters. In an embodiment, the one or moreprocessing parameters may be determined immediately (e.g., beforeprocessing the one or more patterns or a next substrate) afterprocessing the one or more patterns or a substrate. The determinedexistence and/or characteristic of a defect may serve as a basis for adecision of whether to inspect the one or more patterns. In anembodiment, the values of the one or more processing parameters may beused to calculate a moving average of the one or more lithographicparameters. A moving average is useful to capture long term drift of theone or more lithographic parameters, without distraction by short termfluctuation.

In optional step 293, the one or more patterns are inspected if thedetermined or predicted existence, probability of existence,characteristic, and/or a combination selected from the foregoing, meetsone or more criteria (e.g., probability is above a threshold).

FIG. 3 shows exemplary sources of the one or more processing parameters350. One source may be data 310 of the processing apparatus, such as aparameter of the illumination system, projection optics, substratestage, etc. of a lithography apparatus. Another source may be data 320from one or more substrate metrology tools, such as a substrate heightmap/data, a focus map/data, critical dimension uniformity (CDU)map/data, etc. Data 320 may be obtained before a substrate is subject toa step (e.g., etch) that prevents reworking of the substrate. Anothersource may be data 330 from one or more patterning device metrologytools, such as a patterning device (e.g., mask) CDU map/data, apatterning device (e.g., mask) film stack parameter variation, etc. Yetanother source may be data 340 from an operator of the processingapparatus.

FIG. 4A shows an implementation of step 292 of FIG. 2. In step 411, theOPW of the one or more patterns is obtained, either by using a model orby querying a database. For example, the OPW may be a space spanned byprocessing parameters such as focus and dose. In step 412, the values ofthe one or more processing parameters determined in step 291 of FIG. 2are compared with the OPW. If the values of the one or more processingparameters fall within the OPW, no defect exists; if the values of theone or more processing parameters fall outside the OPW, at least onedefect is expected to exist.

FIG. 4B shows a further implementation of step 292 of FIG. 2. The one ormore processing parameters 420 may be used as input (e.g., anindependent variable) to a classification model 430. The one or moreprocessing parameters 420 may include a characteristic of theillumination (e.g., intensity, pupil profile, etc.), a characteristic ofthe projection optics, dose, focus, a characteristic of the resist, acharacteristic of development and/or post-exposure baking of the resist,and/or a characteristic of etching. The term “classifier” or“classification model” sometimes also refers to a mathematical function,implemented by a classification algorithm, which maps input data to acategory. In machine learning and statistics, classification is theproblem of identifying to which of a set of categories 440(sub-populations) a new observation belongs, on the basis of a trainingset of data containing observations (or instances) whose categorymembership is known. The individual observations are analyzed into a setof quantifiable properties, known as various explanatory variables,features, etc. These properties may variously be categorical (e.g.“good”—a lithographic process that does not produce a defect or “bad”—alithographic process that produces a defect; “type 1”, “type 2”, . . .“type n”—different types of defects). Classification is considered aninstance of supervised learning, i.e. learning where a training set ofcorrectly identified observations is available. Examples ofclassification models are logistic regression, multinomial logit, probitregression, the perceptron algorithm, support vector machine, importvector machine, and/or linear discriminant analysis.

FIG. 5 shows an exemplary flow that uses the method of FIG. 2. In step510, step 291 is carried out on one or more patterns produced onto aportion, or an entirety, of a substrate, where, in this example, thevalues of the one or more processing parameters are determined by usingone or more metrology tools on the patterning device and/or thesubstrate, and by obtaining (e.g., measuring) values of one or moreparameters of the device manufacturing process or equipment (e.g., alithographic apparatus). In step 520, step 292 is carried out, where, inthis example, if the values of the one or more processing parameters forthe one or more patterns are beyond one or more thresholds, at least onedefect produced by the one or more patterns is expected to exist. Instep 530, an area that encloses the one or more patterns that isexpected to produce at least one defect (“a location of interest (LOI)”)is identified. A LOI may also enclose one or more patterns that are notexpected to produce at least one defect. The one or more LOIs may becompiled as a map and presented to a user. In step 540, the one or morepatterns in the one or more LOIs are inspected.

FIG. 6 shows another exemplary flow that uses the method of FIG. 2. Instep 610, step 291 is carried out on one or more patterns produced ontoa portion, or an entirety, of a substrate, where, in this example, thevalues of the one or more processing parameters are determined by usingone or more metrology tools on the patterning device and/or thesubstrate, and by obtaining (e.g., measuring) values of one or moreparameters of the device manufacturing process or equipment (e.g., alithographic apparatus). In step 620, step 292 is carried out, where, inthis example, if the values of the one or more processing parameters forthe one or more patterns are beyond an OPW 650 of the one or morepatterns or an OPW 651 of all the patterns, at least one defect producedby the one or more patterns is expected to exist. In step 630, an area(“a location of interest (LOI)”) that encloses the one or more patternsthat is expected to produce at least one defect is identified. A LOI mayalso enclose one or more patterns that are not expected to produce atleast one defect. The one or more LOIs may be compiled as a map andpresented to a user. In step 640, the one or more patterns in the one ormore LOIs are inspected.

In an embodiment, a computer-implemented defect prediction method for adevice manufacturing process may include: determining values of one ormore processing parameters under which one or more patterns areprocessed onto an area of a substrate; and determining or predicting,using the values of the one or more processing parameters, existence,probability of existence, a characteristic, and/or a combinationselected from the foregoing, of a defect in the area, produced from theone or more patterns with the device manufacturing process. Determiningor predicting the existence, probability of existence, characteristic,and/or combination selected from the foregoing, may be done by comparinga process window of the one or more patterns with the values of the oneor more processing parameters. The method may further include inspectingthe area if the determined or predicted existence, probability ofexistence, characteristic, and/or combination selected from theforegoing, meets one or more criteria. The one or more criteria maycomprise that the values of the one or more processing parameters falloutside of a process window of the one or more patterns.

In an embodiment, a device for selecting areas to be inspected on asubstrate may obtain values of one or more processing parameters underwhich one or more patterns are processed onto an area of the substrate,select an area for inspection, if existence, probability of existence, acharacteristic, and/or a combination selected from the foregoing, of adefect, in the area, produced from the one or more patterns, meets oneor more criteria. The existence, probability of existence,characteristic, and/or combination selected from the foregoing may bedetermined or predicted using the values of the one or more processingparameters. The device may export a file including a data structurerepresenting the area.

A substrate inspection tool may inspect patterns processed onto thesubstrate. The substrate inspection tool may obtain a file including adata structure representing an area (instead of, or in addition to,specific locations) to be inspected on the substrate. The substrateinspection tool may inspect the area.

In an embodiment, a computer-implemented defect prediction method for adevice manufacturing process involving processing one or more patternsonto a substrate, the method comprising: determining values of one ormore processing parameters across the substrate, and identifying one ormore areas, utilizing the values of the one or more processingparameters, as being one or more areas to inspect.

The one or more processing parameters may be one or more globalprocessing parameters. The one or more areas may be identified withoutusing any characteristic of the one or more patterns.

The one or more areas identified may have increased probability ofexistence of a defect produced with the device manufacturing process(e.g., with higher probability than another part (e.g., the rest) of thesubstrate).

FIG. 7 is a block diagram that illustrates a computer system 100 whichcan assist in implementing the methods and flows disclosed herein.Computer system 100 includes a bus 102 or other communication mechanismfor communicating information, and a processor 104 (or multipleprocessors 104 and 105) coupled with bus 102 for processing information.Computer system 100 also includes a main memory 106, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 102for storing information and instructions to be executed by processor104. Main memory 106 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 104. Computer system 100 further includes a readonly memory (ROM) 108 or other static storage device coupled to bus 102for storing static information and instructions for processor 104. Astorage device 110, such as a magnetic disk or optical disk, is providedand coupled to bus 102 for storing information and instructions.

Computer system 100 may be coupled via bus 102 to a display 112, such asa cathode ray tube (CRT) or flat panel or touch panel display fordisplaying information to a computer user. An input device 114,including alphanumeric and other keys, is coupled to bus 102 forcommunicating information and command selections to processor 104.Another type of user input device is cursor control 116, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 104 and for controllingcursor movement on display 112. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Atouch panel (screen) display may also be used as an input device.

According to one embodiment, portions or all of a process describedherein may be performed by computer system 100 in response to processor104 executing one or more sequences of one or more instructionscontained in main memory 106. Such instructions may be read into mainmemory 106 from another computer-readable medium, such as storage device110. Execution of the sequences of instructions contained in main memory106 causes processor 104 to perform the process steps described herein.One or more processors in a multi-processing arrangement may also beemployed to execute the sequences of instructions contained in mainmemory 106. In alternative embodiments, hard-wired circuitry may be usedin place of or in combination with software instructions. Thus,embodiments are not limited to any specific combination of hardwarecircuitry and software.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to processor 104 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media include, for example, optical or magnetic disks, suchas storage device 110. Volatile media include dynamic memory, such asmain memory 106. Transmission media include coaxial cables, copper wireand fiber optics, including the wires that comprise bus 102.Transmission media can also take the form of acoustic or light waves,such as those generated during radio frequency (RF) and infrared (IR)data communications. Common forms of computer-readable media include,for example, a floppy disk, a flexible disk, hard disk, magnetic tape,any other magnetic medium, a CD-ROM, DVD, any other optical medium,punch cards, paper tape, any other physical medium with patterns ofholes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip orcartridge, a carrier wave as described hereinafter, or any other mediumfrom which a computer can read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to processor 104 forexecution. For example, the instructions may initially be borne on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 100 canreceive the data on the telephone line and use an infrared transmitterto convert the data to an infrared signal. An infrared detector coupledto bus 102 can receive the data carried in the infrared signal and placethe data on bus 102. Bus 102 carries the data to main memory 106, fromwhich processor 104 retrieves and executes the instructions. Theinstructions received by main memory 106 may optionally be stored onstorage device 110 either before or after execution by processor 104.

Computer system 100 may also include a communication interface 118coupled to bus 102. Communication interface 118 provides a two-way datacommunication coupling to a network link 120 that is connected to alocal network 122. For example, communication interface 118 may be anintegrated services digital network (ISDN) card or a modem to provide adata communication connection to a corresponding type of telephone line.As another example, communication interface 118 may be a local areanetwork (LAN) card to provide a data communication connection to acompatible LAN. Wireless links may also be implemented. In any suchimplementation, communication interface 118 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 120 typically provides data communication through one ormore networks to other data devices. For example, network link 120 mayprovide a connection through local network 122 to a host computer 124 orto data equipment operated by an Internet Service Provider (ISP) 126.ISP 126 in turn provides data communication services through theworldwide packet data communication network, now commonly referred to asthe “Internet” 128. Local network 122 and Internet 128 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 120 and through communication interface 118, which carrythe digital data to and from computer system 100, are exemplary forms ofcarrier waves transporting the information.

Computer system 100 can send messages and receive data, includingprogram code, through the network(s), network link 120, andcommunication interface 118. In the Internet example, a server 130 mighttransmit a requested code for an application program through Internet128, ISP 126, local network 122 and communication interface 118. Inaccordance with one or more embodiments, one such downloaded applicationprovides for the process of an embodiment described herein, for example.The received code may be executed by processor 104 as it is received,and/or stored in storage device 110, or other non-volatile storage forlater execution. In this manner, computer system 100 may obtainapplication code in the form of a carrier wave.

FIG. 8 schematically depicts another exemplary lithographic apparatus1000 utilizing the methods described herein or to which the methods maybe applied. The lithographic apparatus 1000 includes:

-   -   a source collector module SO    -   an illumination system (illuminator) IL configured to condition        a radiation beam B (e.g. EUV radiation).    -   a support structure (e.g. a patterning device table) MT        constructed to support a patterning device (e.g. a mask or a        reticle) MA and connected to a first positioner PM configured to        accurately position the patterning device;    -   a substrate table (e.g. a substrate table) WT constructed to        hold a substrate (e.g. a resist coated wafer) W and connected to        a second positioner PW configured to accurately position the        substrate; and    -   a projection system (e.g. a reflective projection system) PS        configured to project a pattern imparted to the radiation beam B        by patterning device MA onto a target portion C (e.g. comprising        one or more dies) of the substrate W.

As here depicted, the apparatus 1000 is of a reflective type (e.g.employing a reflective mask). It is to be noted that because mostmaterials are absorptive within the EUV wavelength range, the mask mayhave multilayer reflectors comprising, for example, a multi-stack ofMolybdenum and Silicon. In one example, the multi-stack reflector has a40 layer pairs of Molybdenum and Silicon where the thickness of eachlayer is a quarter wavelength. Even smaller wavelengths may be producedwith X-ray lithography. Since most material is absorptive at EUV andx-ray wavelengths, a thin piece of patterned absorbing material on thepatterning device topography (e.g., a TaN absorber on top of themulti-layer reflector) defines where features would print (positiveresist) or not print (negative resist).

Referring to FIG. 8, the illuminator IL receives an extreme ultra violetradiation beam from the source collector module SO. Methods to produceEUV radiation include, but are not necessarily limited to, converting amaterial into a plasma state that has at least one element, e.g., xenon,lithium or tin, with one or more emission lines in the EUV range. In onesuch method, often termed laser produced plasma (“LPP”) the plasma canbe produced by irradiating a fuel, such as a droplet, stream or clusterof material having the line-emitting element, with a laser beam. Thesource collector module SO may be part of an EUV radiation systemincluding a laser, not shown in FIG. 8, for providing the laser beamexciting the fuel. The resulting plasma emits output radiation, e.g.,EUV radiation, which is collected using a radiation collector, disposedin the source collector module. The laser and the source collectormodule may be separate entities, for example when a CO2 laser is used toprovide the laser beam for fuel excitation.

In such cases, the laser is not considered to form part of thelithographic apparatus and the radiation beam is passed from the laserto the source collector module with the aid of a beam delivery systemcomprising, for example, suitable directing mirrors and/or a beamexpander. In other cases the source may be an integral part of thesource collector module, for example when the source is a dischargeproduced plasma EUV generator, often termed as a DPP source.

The illuminator IL may comprise an adjuster for adjusting the angularintensity distribution of the radiation beam. Generally, at least theouter and/or inner radial extent (commonly referred to as σ-outer andσ-inner, respectively) of the intensity distribution in a pupil plane ofthe illuminator can be adjusted. In addition, the illuminator IL maycomprise various other components, such as facetted field and pupilmirror devices. The illuminator may be used to condition the radiationbeam, to have a desired uniformity and intensity distribution in itscross section.

The radiation beam B is incident on the patterning device (e.g., mask)MA, which is held on the support structure (e.g., mask table) MT, and ispatterned by the patterning device. After being reflected from thepatterning device (e.g. mask) MA, the radiation beam B passes throughthe projection system PS, which focuses the beam onto a target portion Cof the substrate W. With the aid of the second positioner PW andposition sensor PS2 (e.g. an interferometric device, linear encoder orcapacitive sensor), the substrate table WT can be moved accurately, e.g.so as to position different target portions C in the path of theradiation beam B. Similarly, the first positioner PM and anotherposition sensor PS1 can be used to accurately position the patterningdevice (e.g. mask) MA with respect to the path of the radiation beam B.Patterning device (e.g. mask) MA and substrate W may be aligned usingpatterning device alignment marks M1, M2 and substrate alignment marksP1, P2.

The depicted apparatus 1000 could be used in at least one of thefollowing modes:

1. In step mode, the support structure (e.g. patterning device table) MTand the substrate table WT are kept essentially stationary, while anentire pattern imparted to the radiation beam is projected onto a targetportion C at one time (i.e. a single static exposure). The substratetable WT is then shifted in the X and/or Y direction so that a differenttarget portion C can be exposed.

2. In scan mode, the support structure (e.g. patterning device table) MTand the substrate table WT are scanned synchronously while a patternimparted to the radiation beam is projected onto a target portion C(i.e. a single dynamic exposure). The velocity and direction of thesubstrate table WT relative to the support structure (e.g. patterningdevice table) MT may be determined by the (de-)magnification and imagereversal characteristics of the projection system PS.

3. In another mode, the support structure (e.g. patterning device table)MT is kept essentially stationary holding a programmable patterningdevice, and the substrate table WT is moved or scanned while a patternimparted to the radiation beam is projected onto a target portion C. Inthis mode, generally a pulsed radiation source is employed and theprogrammable patterning device is updated as required after eachmovement of the substrate table WT or in between successive radiationpulses during a scan. This mode of operation can be readily applied tomaskless lithography that utilizes programmable patterning device, suchas a programmable mirror array of a type as referred to above.

FIG. 9 shows the apparatus 1000 in more detail, including the sourcecollector module SO, the illumination system IL, and the projectionsystem PS. The source collector module SO is constructed and arrangedsuch that a vacuum environment can be maintained in an enclosingstructure 220 of the source collector module SO. An EUV radiationemitting plasma 210 may be formed by a discharge produced plasma source.EUV radiation may be produced by a gas or vapor, for example Xe gas, Livapor or Sn vapor in which the very hot plasma 210 is created to emitradiation in the EUV range of the electromagnetic spectrum. The very hotplasma 210 is created by, for example, an electrical discharge causingan at least partially ionized plasma. Partial pressures of, for example,10 Pa of Xe, Li, Sn vapor or any other suitable gas or vapor may berequired for efficient generation of the radiation. In an embodiment, aplasma of excited tin (Sn) is provided to produce EUV radiation.

The radiation emitted by the hot plasma 210 is passed from a sourcechamber 211 into a collector chamber 212 via an optional gas barrier orcontaminant trap 230 (in some cases also referred to as contaminantbarrier or foil trap) which is positioned in or behind an opening insource chamber 211. The contaminant trap 230 may include a channelstructure. Contamination trap 230 may also include a gas barrier or acombination of a gas barrier and a channel structure. The contaminanttrap or contaminant barrier 230 further indicated herein at leastincludes a channel structure, as known in the art.

The collector chamber 211 may include a radiation collector CO which maybe a so-called grazing incidence collector. Radiation collector CO hasan upstream radiation collector side 251 and a downstream radiationcollector side 252. Radiation that traverses collector CO can bereflected off a grating spectral filter 240 to be focused in a virtualsource point IF along the optical axis indicated by the dot-dashed line‘O’. The virtual source point IF is commonly referred to as theintermediate focus, and the source collector module is arranged suchthat the intermediate focus IF is located at or near an opening 221 inthe enclosing structure 220. The virtual source point IF is an image ofthe radiation emitting plasma 210.

Subsequently the radiation traverses the illumination system IL, whichmay include a facetted field mirror device 22 and a facetted pupilmirror device 24 arranged to provide a desired angular distribution ofthe radiation beam 21, at the patterning device MA, as well as a desireduniformity of radiation intensity at the patterning device MA. Uponreflection of the beam of radiation 21 at the patterning device MA, heldby the support structure MT, a patterned beam 26 is formed and thepatterned beam 26 is imaged by the projection system PS via reflectiveelements 28, 30 onto a substrate W held by the substrate table WT.

More elements than shown may generally be present in illumination opticsunit IL and projection system PS. The grating spectral filter 240 mayoptionally be present, depending upon the type of lithographicapparatus. Further, there may be more mirrors present than those shownin the figures, for example there may be 1-6 additional reflectiveelements present in the projection system PS than shown in FIG. 9.

Collector optic CO, as illustrated in FIG. 9, is depicted as a nestedcollector with grazing incidence reflectors 253, 254 and 255, just as anexample of a collector (or collector mirror). The grazing incidencereflectors 253, 254 and 255 are disposed axially symmetric around theoptical axis O and a collector optic CO of this type is preferably usedin combination with a discharge produced plasma source, often called aDPP source.

Alternatively, the source collector module SO may be part of an LPPradiation system as shown in FIG. 10. A laser LA is arranged to depositlaser energy into a fuel, such as xenon (Xe), tin (Sn) or lithium (Li),creating the highly ionized plasma 210 with electron temperatures ofseveral 10's of eV. The energetic radiation generated duringde-excitation and recombination of these ions is emitted from theplasma, collected by a near normal incidence collector optic CO andfocused onto the opening 221 in the enclosing structure 220.

The concepts disclosed herein may simulate or mathematically model anygeneric imaging system for imaging sub wavelength features, and may beespecially useful with emerging imaging technologies capable ofproducing wavelengths of an increasingly smaller size. Emergingtechnologies already in use include EUV (extreme ultra violet)lithography that is capable of producing a 193 nm wavelength with theuse of an ArF laser, and even a 157 nm wavelength with the use of aFluorine laser. Moreover, EUV lithography is capable of producingwavelengths within a range of 20-5 nm by using a synchrotron or byhitting a material (either solid or a plasma) with high energy electronsin order to produce photons within this range.

While the concepts disclosed herein may be used for imaging on asubstrate such as a silicon wafer, it shall be understood that thedisclosed concepts may be used with any type of lithographic imagingsystems, e.g., those used for imaging on substrates other than siliconwafers.

The invention may further be described using the following clauses:

-   1. A computer-implemented defect prediction method for a device    manufacturing process involving processing one or more patterns onto    a substrate, the method comprising: determining values of one or    more processing parameters under which the one or more patterns are    processed; and-   determining or predicting, using the values of the one or more    processing parameters, existence, probability of existence, a    characteristic, and/or a combination selected from the foregoing, of    a defect resulting from production of the one or more patterns with    the device manufacturing process.-   2. The method of clause 1, further comprising inspecting the one or    more patterns if the determined or predicted existence, probability    of existence, characteristic, and/or a combination selected from the    foregoing, meets one or more criteria.-   3. The method of clause 1 or clause 2, further comprising    identifying one or more areas that enclose the one or more patterns    if the determined or predicted existence, probability of existence,    characteristic, and/or combination selected from the foregoing,    meets one or more criteria.-   4. The method of any of clauses 1 to 3, wherein the determining or    predicting the existence, probability of existence, characteristic,    and/or combination selected from the foregoing, further uses a    characteristic of the one or more patterns.-   5. The method of any of clauses 1 to 4, wherein the one or more    patterns comprise a process window limiting pattern.-   6. The method of any of clauses 1 to 5, wherein determining or    predicting the existence, probability of existence, characteristic,    and/or combination selected from the foregoing comprises comparing    the values of the one or more processing parameters with an    overlapping process window of the one or more patterns.-   7. The method of any of clauses 1 to 5, wherein determining or    predicting the existence, probability of existence, characteristic,    and/or combination selected from the foregoing comprises determining    whether the values of the one or more processing parameters are    beyond one or more thresholds.-   8. The method of any of clauses 1 to 5, wherein determining or    predicting the existence, probability of existence, characteristic,    and/or combination selected from the foregoing comprises using a    classification model with the values of the one or more processing    parameters as input to the classification model.-   9. The method of clause 8, wherein the classification model is one    or more selected from the following: logistic regression,    multinomial logit, probit regression, the perceptron algorithm,    support vector machine, import vector machine, and/or linear    discriminant analysis.-   10. The method of any of clauses 1 to 5, wherein determining or    predicting the existence, probability of existence, characteristic,    and/or combination selected from the foregoing, of the defect    comprises simulating an image, or expected patterning contours, of    the one or more patterns under the values of the one or more    processing parameters and determining an image or contour parameter.-   11. The method of any of clauses 1 to 10, wherein the one or more    patterns are identified using an empirical model or a computational    model.-   12. The method of any of clauses 1 to 11, wherein the one or more    processing parameters are selected from: focus, depth of focus,    focus error, dose, an illumination parameter, a projection optics    parameter, data obtained from metrology, and/or data from an    operator of a processing apparatus used in the device manufacturing    process.-   13. The method of any of clauses 1 to 12, wherein the values of the    one or more processing parameters are obtained from metrology.-   14. The method of any of clauses 1 to 13, wherein the one or more    processing parameters are determined or predicted using a model or    by querying a database.-   15. The method of any of clauses 1 to 14, wherein the device    manufacturing process comprises using a lithography apparatus.-   16. The method of any of clauses 1 to 15, wherein the device    manufacturing process comprises etching the substrate.-   17. The method of any of clauses 1 to 16, wherein the values of the    one or more processing parameters are determined, before,    immediately before or during the one or more patterns are processed.-   18. The method of any of clauses 1 to 17, wherein the one or more    processing parameters comprise one or more local processing    parameters, one or more global processing parameters, and/or a    combination selected from the foregoing.-   19. The method of any of clauses 1 to 18, wherein the defect is    undetectable before the substrate is irreversibly processed.-   20. The method of any of clauses 1 to 19, wherein the defect is one    or more selected from: necking, line pull back, line thinning,    critical dimension error, overlapping, resist top loss, resist    undercut and/or bridging.-   21. The method of any of clause 1 to 20, wherein the one or more    patterns comprise process window limiting patterns (PWLPs).-   22. A computer-implemented defect prediction method for a device    manufacturing process, the method comprising:-   determining values of one or more processing parameters under which    one or more patterns are processed onto an area of a substrate; and-   determining or predicting, using the values of the one or more    processing parameters, existence, probability of existence, a    characteristic, and/or a combination selected from the foregoing, of    a defect, in the area, resulting from production of the one or more    patterns with the device manufacturing process.-   23. The method of clause 22, wherein determining or predicting the    existence, probability of existence, characteristic, and/or a    combination selected from the foregoing, comprises comparing a    process window of the one or more patterns with the values of the    one or more processing parameters.-   24. The method of clause 22 or clause 23, further comprising    inspecting the area if the determined or predicted existence,    probability of existence, characteristic, and/or combination    selected from the foregoing, meets one or more criteria.-   25. The method of clause 24, wherein the one or more criteria    comprise that the values of the one or more processing parameters    fall outside of a process window of the one or more patterns.-   26. A computer-implemented defect prediction method for a device    manufacturing process involving processing one or more patterns onto    a substrate, the method comprising:-   determining values across the substrate of one or more processing    parameters of the device manufacturing process under which one or    more patterns are processed onto the substrate, and-   identifying one or more areas, utilizing the values of the one or    more processing parameters, as having, or having increased    probability of existence of, a defect resulting from production with    the device manufacturing process.-   27. The method of clause 26, wherein the identifying comprises:-   comparing a process window associated with the one or more patterns    located at the identified one or more areas with the values of the    one or more processing parameters at the identified one or more    areas, and-   determining or predicting, using the comparison, existence,    probability of existence, a characteristic, or a combination    thereof, of a defect produced from the one or more patterns at the    identified one or more areas with the device manufacturing process.-   28. A computer-implemented defect prediction method for a device    manufacturing process, the method comprising:-   determining values of one or more processing parameters under which    one or more patterns are processed onto an area of a substrate; and-   determining or predicting, using the values of the one or more    processing parameters, a sub-area of the substrate at which a defect    exists, or has an increased probability of existing, resulting from    production of the one or more patterns with the device manufacturing    process.-   29. A device for selecting areas to be inspected on a substrate,-   the device configured to obtain values of one or more processing    parameters under which one or more patterns are processed onto an    area of the substrate; and-   the device configured to select an area for inspection, if    existence, probability of existence, a characteristic, and/or a    combination selected from the foregoing, of a defect in the area    resulting from production of the one or more patterns, meets one or    more criteria,-   wherein the existence, probability of existence, characteristic,    and/or combination selected from the foregoing, is determined or    predicted using the values of the one or more processing parameters.-   30. The device of clause 29, wherein the device is further    configured to export a file including a data structure representing    the area.-   31. A substrate inspection tool configured to inspect patterns    processed onto a substrate, wherein the substrate inspection tool is    configured to obtain a file including a data structure representing    an area comprising the patterns to be inspected on the substrate,    wherein the substrate inspection tool is configured to inspect the    area.-   32. A computer-implemented defect prediction method for a device    manufacturing process involving processing one or more patterns onto    a substrate, the method comprising:-   determining values of one or more processing parameters across the    substrate, and-   identifying one or more areas, utilizing the values of the one or    more processing parameters, for inspection.-   33. The method of clause 32, wherein the one or more processing    parameters are one or more global processing parameters.-   34. The method of any clause 32 or clause 33, wherein the one or    more areas are identified without using any characteristic of the    one or more patterns.-   35. The method any of clauses 32 to 34, wherein the one or more    areas identified have higher probability of existence of a defect    produced with the device manufacturing process than another part of    the substrate.-   36. The method of any of clauses 32 to 35, further comprising    inspecting the one or more areas.-   37. The method of any of clauses 32 to 36, further comprising    exporting a file including a data structure representing the one or    more areas.-   38. The method of any of clauses 32 to 37, wherein the one or more    processing parameters are selected from: focus, depth of focus,    focus error, dose, an illumination parameter, a projection optics    parameter, data obtained from metrology, and/or data from an    operator of a processing apparatus used in the device manufacturing    process.-   39. The method of any of clauses 32 to 38, wherein the values of the    one or more processing parameters are obtained from metrology.-   40. The method of any of clauses 32 to 39, wherein the one or more    processing parameters are determined or predicted using a model or    by querying a database.-   41. The method of any of clauses 32 to 40, wherein the device    manufacturing process comprises using a lithography apparatus.-   42. The method of any of clauses 32 to 41, wherein the device    manufacturing process comprises etching the substrate.-   43. A computer program product comprising a computer readable medium    having instructions recorded thereon, the instructions when executed    by a computer implementing the method of any of clauses 1 to 28 or    32 to 42.

Aspects of the invention can be implemented in any convenient form. Forexample, an embodiment may be implemented by one or more appropriatecomputer programs which may be carried on an appropriate carrier mediumwhich may be a tangible carrier medium (e.g. a disk) or an intangiblecarrier medium (e.g. a communications signal). Embodiments of theinvention may be implemented using suitable apparatus which mayspecifically take the form of a programmable computer running a computerprogram arranged to implement a method as described herein.

The descriptions above are intended to be illustrative, not limiting.Thus, it will be apparent to one skilled in the art that modificationsmay be made to the embodiments as described without departing from thescope of the claims set out below.

What is claimed is:
 1. A defect prediction method for a devicemanufacturing process involving processing one or more patterns onto asubstrate, the defect prediction method comprising: determining valuesof one or more processing parameters under which the one or morepatterns are processed; determining or predicting, by a hardwarecomputer system using the values of the one or more processingparameters, an existence, a probability of existence, a characteristic,and/or a combination selected from the foregoing, of a defect resultingfrom production of the one or more patterns with the devicemanufacturing process; and generating electronic data representing oneor more sub-areas of a surface of the substrate for inspection by aphysical inspection apparatus, the one or more sub-areas enclose the oneor more patterns for which the determined or predicted existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, meets one or more criteria and enclose one or moreother patterns not identified as prone to produce a defect.
 2. Themethod of claim 1, further comprising inspecting the one or morepatterns responsive to the determined or predicted existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, meeting one or more criteria.
 3. The method of claim1, wherein the determining or predicting the existence, probability ofexistence, characteristic, and/or combination selected from theforegoing, of the defect further uses a characteristic of the one ormore patterns.
 4. The method of claim 1, wherein the one or morepatterns comprise a process window limiting pattern.
 5. The method ofclaim 1, wherein the determining or predicting the existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, of the defect comprises: comparing the values of theone or more processing parameters with an overlapping process window ofthe one or more patterns, or determining whether the values of the oneor more processing parameters are beyond one or more thresholds, orusing a classification model with the values of the one or moreprocessing parameters as input to the classification model.
 6. Themethod of claim 1, wherein the determining or predicting the existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, of the defect comprises simulating an image, orexpected patterning contour, of the one or more patterns under thevalues of the one or more processing parameters and determining an imageor contour parameter.
 7. The method of claim 1, wherein the one or morepatterns are identified using an empirical model or a computationalmodel.
 8. The method of claim 1, wherein the one or more processingparameters are selected from: focus, depth of focus, focus error, dose,an illumination parameter, a projection optics parameter, data obtainedfrom metrology, and/or data from an operator of a processing apparatusused in the device manufacturing process.
 9. The method of claim 1,wherein the values of the one or more processing parameters are obtainedfrom metrology.
 10. The method of claim 1, wherein the one or moreprocessing parameters are determined or predicted using a model or byquerying a database.
 11. The method of claim 1, wherein the one or moreprocessing parameters comprise one or more local processing parameters,one or more global processing parameters, and/or a combination selectedfrom the foregoing.
 12. The method of claim 1, wherein the defect isundetectable before the substrate is irreversibly processed.
 13. Adevice for selecting areas to be inspected on a substrate, the deviceconfigured to obtain values of one or more processing parameters underwhich one or more patterns are processed onto an area of the substrate;and the device configured to select a sub-area of a surface of thesubstrate for inspection by a physical inspection apparatus, responsiveto an existence, a probability of existence, a characteristic, and/or acombination selected from the foregoing, of a defect in the arearesulting from production of the one or more patterns, meets one or morecriteria and generate electronic data representing the sub-area, for useby an apparatus to enable the inspection, wherein the existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, is determined or predicted using the values of theone or more processing parameters, and wherein the sub-area forinspection includes a pattern prone to produce a defect and a patternnot identified as prone to produce a defect.
 14. The device of claim 13,wherein the device is further configured to export a file including adata structure representing the sub-area.
 15. The device of claim 13,wherein the device is further configured to cause inspection of the oneor more patterns responsive to the determined or predicted existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, meeting one or more criteria.
 16. The device ofclaim 13, wherein the determination or prediction of the existence,probability of existence, characteristic, and/or combination selectedfrom the foregoing, of the defect further uses a characteristic of theone or more patterns.
 17. The device of claim 13, wherein the one ormore patterns comprise a process window limiting pattern.
 18. A methodfor a device manufacturing process involving processing one or morepatterns onto a substrate, the method comprising: determining valuesacross the substrate of one or more processing parameters of the devicemanufacturing process under which one or more patterns are processedonto the substrate; identifying one or more sub-areas of a surface ofthe substrate for inspection by a physical inspection apparatus, by ahardware computer system utilizing the values of the one or moreprocessing parameters, as having, or having increased probability ofexistence of, a defect resulting from production with the devicemanufacturing process, wherein the one or more sub-areas for inspectionincludes a pattern prone to produce a defect and a pattern notidentified as prone to produce a defect; and generating electronic datarepresenting the one or more sub-areas, for use by an apparatus toenable the inspection.
 19. The method of claim 18, wherein theidentifying comprises: comparing a process window associated with theone or more patterns located at the identified one or more sub-areaswith the values of the one or more processing parameters at theidentified one or more sub-areas, and determining or predicting, usingthe comparison, an existence, a probability of existence, acharacteristic, or a combination thereof, of a defect produced from theone or more patterns at the identified one or more sub-areas with thedevice manufacturing process.
 20. The method of claim 18, furthercomprising inspecting the one or more sub-areas based on the electronicdata.